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基于网络效率的城市轨道交通网络抗震韧性评估

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该文基于震后轨道交通网络主体结构的破坏场景,以恢复震后通行能力为目标,分析了地震灾害作用下城市轨道交通网络的韧性.采用Space L方法构建轨道交通网络模型,选用客流加权的网络平均效率表示网络连通性能.采用地震易损性模型计算网络主要结构单元(车站、隧道和桥梁)的破坏概率,采用Monte Carlo方法模拟网络主体结构单元的震后状态;考虑破坏单元修复时间存在不确定性,模拟了破坏单元的震后修复过程;根据震后不同时刻网络性能曲线和韧性三角形,定量化评估轨道交通网络抗震韧性指数和韧性损失.以北京地区城市轨道交通网络为例,分析了地震烈度、恢复策略和修复队伍数量对网络连通性能、恢复过程和韧性指标的影响.结果表明:轨道交通网络在遭受地震破坏、蓄意攻击和随机破坏后的韧性特征存在差异;震前提升少量重要单元的可靠度可提升网络灾前抵抗能力,但对网络灾后恢复能力和韧性指数的提升效果不明显.该研究可为城市轨道交通系统抗震安全性评估提供理论支撑和参考.
Seismic resilience evaluation of urban rail transit network based on network efficiency
[Objective]Seismic damage and destruction of the stations,tunnels,and other structures considerably impair the functionality of the urban rail transit system.Current research on the system performance of the rail transit network primarily focuses on the scenarios of intentional attack and stochastic damage,which is dramatically different from the earthquake disaster scenarios.This paper proposed a quantitative framework to evaluate the seismic performance and resilience of rail transit networks.[Methods]The seismic fragility model was used to calculate the failure probability of the primary structural elements,including stations,tunnels,and bridges of the rail transit system.The graphical model of the network was established using the Space L modeling method.This approach was used to depict the interdependency of system elements.The network performance was expressed by the network efficiency weighted by passenger flow between rail transit stations.The Monte Carlo simulation was used to assess the uncertainty of the earthquake damage state of structures and the post-earthquake recovery of the damaged elements.According to the network performance curves during the post-earthquake recovery process,the seismic resilience index and resilience loss of the rail transit network were quantitatively evaluated using the concept of resilience triangle.Considering the Beijing rail transit network,the effects of earthquake intensity,recovery strategy on network performance,and resilience indexes were investigated.[Results]The results of the present analysis were as follows.(1)The resilience characteristics of rail transit networks under earthquakes,intentional attacks,and stochastic damage were different.The resilience index under earthquake damage was 0.936 3,whereas the resilience index under stochastic damage was 0.934 0.The resilience index under intentional attack was 0.863 4.(2)In the damage scenario corresponding to different earthquake intensities,the system resilience index calculated by the recovery sequence sorted by the dynamic importance of damaged elements were larger than that sorted by the static importance of damaged elements.Moreover,the damage scenario involving several damaged elements typically results in a larger difference between the resilience index calculated by the two recovery strategies.(3)Pre-earthquake enhancement measures to reduce the failure probability of crucial elements could effectively enhance the disaster resistance capacity of the network;however,their influence on improving the post-earthquake recovery capacity remained unclear.[Conclusions]Based on the seismic fragility models of the primary structure of the rail transit network,the graphical model of the network,and the importance of ranking-based post-earthquake recovery of the damaged elements,this paper establishes a framework to quantitatively evaluate the seismic resilience of rail transit network by the passenger-weighted network efficiency.When evaluating network resilience and comparing antiseismic improvement measures,multiple indicators such as resilience index,resilience loss,and recovery duration should be comprehensively analyzed.This framework can provide a reference for the seismic performance evaluation of the urban rail transit network and help decision-makers in allocating maintenance resources to restore the operation function of the urban rail transit system in a timely and cost-effective manner during the recovery process.

urban rail transit networkearthquake disasternetwork efficiencypost-earthquake recoveryresilience evaluation

侯本伟、游丹、范世杰、许成顺、钟紫蓝

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北京工业大学 城市建设学部,北京 100124

城市轨道交通网络 地震灾害 网络效率 震后恢复 韧性评估

国家重点研发计划国家自然科学基金

2022YFC300360352220105011

2024

清华大学学报(自然科学版)
清华大学

清华大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.586
ISSN:1000-0054
年,卷(期):2024.64(3)
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